 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O15943 from www.uniprot.org...
The NucPred score for your sequence is 0.49 (see score help below)
1 MAARRCLNQLRQRYITNRFNICTCAIFLISLPFILAIEETTFAGLSAENA 50
51 ARMLAGSPGDVEKSSLSHHSEMSLVLPHDTYPGFSIKKFKTHPVKINGSS 100
101 HSGAAAYHMLDTDYSKYFTVLEDGVVMTTADISPLVNRPVQLVVVEQTPN 150
151 ATNTHNLQLFVMHRNDMLRFSGSLLDASGEVRENQPAGTRVRGVPLMQAF 200
201 SGSILDEELATPKKVRYTIIDGNVDDAFALQERKANKNIQISAKSLVING 250
251 DDESGVWLVTNRPLDREERAHYDLSVEASDVDGLDRTVSKIQITVLDEND 300
301 NRPIFKSLDYKFAIAGQKSASMESNSSVTYQRFAIMGKVEATDADGDKIA 350
351 YRLKSPSNVVIIVPQTGEIMLAGEPTSNELLIEVIAHDLRYPSLVSAKPA 400
401 KVLLEFLAAEPVSFIMQHLEHDDINNHSHHREKRRVTRAVRPTKRIEFTE 450
451 ADGDTEGKSVFQLEKETDKETFKIRDDNPWVTVETNGAVRVKKKWDYEEL 500
501 GPEKTIDFWVIITNMGHNAGIKYTDNQRVIILVKDVNDEPPYFINRPLPM 550
551 QAVVQLNAPPNTPVFTLQARDPDTDHNIHYFIVRDRTGGRFEVDERSGVV 600
601 RTRGTDLFQLDMEYVLYVKAEDQNGKVDDRRFQSTPEERLSIVGGKRAPQ 650
651 FYMPSYEAEIPENQKKDSDIISIKAKSFADREIRYTLKAQGQGAGTFNIG 700
701 PTSGIVKLAKELDFEDLRQPHVYSLIVTATEDSGGFSTSVDLTIRVTDVN 750
751 DNAPKFELPDYQAHNVDEDIPLGTSILRVKAMDSDSGSNAEIEYLVSDDH 800
801 FAVDSNGIIVNNKQLDADNNNAYYEFIVTAKDKGEPPKSGVATVRVYTKN 850
851 KNDEEPKFSQQVYTPNVDENAGPNTLVTTVVASDKDGDNVRFGFVGGGTS 900
901 SGQFVIEDITGVIRLHNKAISLDKDKYELNVTAMDDGSCCVNGDQTIHTS 950
951 TAVVVVFITDVNDNKPVFKDCSTYYPKVEEGAPNGSPVIKVVATDEDKGV 1000
1001 NGQVKYSIVQQPNQKGTKFTVDEETGEVSTNKVFDREGDDGKFVSVTVKA 1050
1051 TDQGDPSLEGVCSFTVEITDVNDNPPLFDRQKYVENVKQDASIGTNILRV 1100
1101 SASDEDADNNGAIVYSLTAPFNPNDLEYFEIQAESGWIVLKKPLDRETYK 1150
1151 LEAMAQDKGYPPLSRTVEVQIDVVDRANNPPVWDHTVYGPIYVKENMPVG 1200
1201 GKVVSIKASSGIEGNPTVFYRLMPGSTAQTNKFHTFYLQQRPDNGDTWAD 1250
1251 IKVNHPLDYESIKEYNLTIRVENNGAQQLASEATVYIMLEDVNDEIPLFT 1300
1301 EREQETVLEGEPIGTKVTQVNAIDKDGTFPNNQVYYYIVDSPRNEGKEFF 1350
1351 EINLQSGEIFTKTVFDREKKGAYALEVEARDGAPSARPNSNGPNSVTKFI 1400
1401 RIGIADKNDNPPYFDKSLYEAEVDENEDIQHTVLTVTAKDHDESSRIRYE 1450
1451 ITSGNIGGAFAVKNMTGAIYVAGALDYETRRRYELRLAASDNLKENYTTV 1500
1501 IIHVKDVNDNPPVFERPTYRTQITEEDDRNLPKRVLQVTATDGDKDRPQN 1550
1551 IVYFLTGQGIDPDNPANSKFDINRTTGEIFVLKPLDRDQPNGRPQWRFTV 1600
1601 FAQDEGGEGLVGYADVQVNLKDINDNAPIFPQGVYFGNVTENGTAGMVVM 1650
1651 TMTAVDYDDPNEGSNARLVYSIEKNVIEEETGSPIFEIEPDTGVIKTAVC 1700
1701 CLDRERTPDYSIQVVAMDGGGLKGTGTASIRVKDINDMPPQFTKDEWFTE 1750
1751 VDETDGTALPEMPILTVTVHDEDETNKFQYKVIDNSGYGADKFTMVRNND 1800
1801 GTGSLKIVQPLDYEDQLQSNGFRFRIQVNDKGEDNDNDKYHVAYSWVVVK 1850
1851 LRDINDNKPHFERANVEVSVFEDTKVGTELEKFKATDPDQGGKSKVSYSI 1900
1901 DRSSDRQRQFAINQNGSVTIQRSLDREVVPRHQVKILAIDDGSPPKTATA 1950
1951 TLTVIVQDINDNAPKFLKDYRPVLPEHVPPRKVVEILATDDDDRSKSNGP 2000
2001 PFQFRLDPSADDIIRASFKVEQDQKGANGDGMAVISSLRSFDREQQKEYM 2050
2051 IPIVIKDHGSPAMTGTSTLTVIIGDVNDNKMQPGSKDIFVYNYQGQSPDT 2100
2101 PIGRVYVYDLDDWDLPDKKFYWEAMEHPRFKLDEDSGMVTMRAGTREGRY 2150
2151 HLRFKVYDRKHTQTDIPANVTVTVREIPHEAVVNSGSVRLSGISDEDFIR 2200
2201 VWNYRTQSMSRSKMDRFRDKLADLLNTERENVDIFSVQLKRKHPPLTDVR 2250
2251 FSAHGSPYYKPVRLNGIVLMHREEIEKDVGINITMVGIDECLYENQMCEG 2300
2301 SCTNSLEISPLPYMVNANKTALVGVRVDTIADCTCGARNFTKPESCRTTP 2350
2351 CHNGGRCVDTRFGPHCSCPVGYTGPRCQQTTRSFRGNGWAWYPPLEMCDE 2400
2401 SHLSLEFITRKPDGLIIYNGPIVPPERDETLISDFIALELERGYPRLLID 2450
2451 FGSGTLELRVKTKKTLDDGEWHRIDLFWDTESIRMVVDFCKSAEIAEMED 2500
2501 GTPPEFDDMSCQARGQIPPFNEYLNVNAPLQVGGLYREQFDQSLYFWHYM 2550
2551 PTAKGFDGCIRNLVHNSKLYDLAHPGLSRNSVAGCPQTEEVCAQTETTAR 2600
2601 CWEHGNCVGSLSEARCHCRPGWTGPACNIPTIPTTFKAQSYVKYALSFEP 2650
2651 DRFSTQVQLRFRTREEYGELFRVSDQHNREYGILEIKDGHLHFRYNLNSL 2700
2701 RTEEKDLWLNAIVVNDGQWHVVKVNRYGSAATLELDGGEGRRYNETFEFV 2750
2751 GHQWLLVDKQEGVYAGGKAEYTGVRTFEVYADYQKSCLDDIRLEGKHLPL 2800
2801 PPAMNGTQWGQATMARNLEKGCPSNKPCSNVICPDPFECVDLWNVYECTC 2850
2851 GEGRIMSPDSKGCMDRNECLDMPCMNGATCINLEPRLRYRCICPDGFWGE 2900
2901 NCELVQEGQTLKLSMGALAAILVCLLIILILVLVFVVYNRRREAHIKYPG 2950
2951 PDDDVRENIINYDDEGGGEDDMTAFDITPLQIPIGGPMPPELAPMKMPIM 3000
3001 YPVMTLMPGQEPNVGMFIEEHKKRADGDPNAPPFDDLRNYAYEGGGSTAG 3050
3051 SLSSLASGTDDEQQEYDYLGAWGPRFDKLANMYGPEAPNPHNTELEL 3097
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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